📌 Dataframe dengan Variabel Numerik
credit_data <- data.frame(
Age = c(30.83, 58.67, 24.50, 27.83, 20.17),
Debt = c(0.000, 4.460, 0.500, 1.540, 5.625),
YearsEmployed = c(1.25, 3.04, 1.50, 3.75, 1.71),
CreditScore = c(1, 6, 0, 5, 0),
ZipCode = c(202, 43, 280, 100, 120),
Income = c(0, 560, 824, 3, 0)
)
# Menampilkan struktur data
str(credit_data)
## 'data.frame': 5 obs. of 6 variables:
## $ Age : num 30.8 58.7 24.5 27.8 20.2
## $ Debt : num 0 4.46 0.5 1.54 5.62
## $ YearsEmployed: num 1.25 3.04 1.5 3.75 1.71
## $ CreditScore : num 1 6 0 5 0
## $ ZipCode : num 202 43 280 100 120
## $ Income : num 0 560 824 3 0
cov_matrix <- cov(credit_data)
print(cov_matrix)
## Age Debt YearsEmployed CreditScore ZipCode
## Age 231.361400 9.345663 6.999375 33.30 -831.0325
## Debt 9.345663 6.187675 0.605225 1.34 -161.4613
## YearsEmployed 6.999375 0.605225 1.182050 2.81 -73.2075
## CreditScore 33.300000 1.340000 2.810000 8.30 -207.0000
## ZipCode -831.032500 -161.461250 -73.207500 -207.00 8612.0000
## Income 2046.972500 -112.313750 -42.775000 11.55 12109.2500
## Income
## Age 2046.9725
## Debt -112.3137
## YearsEmployed -42.7750
## CreditScore 11.5500
## ZipCode 12109.2500
## Income 151957.8000
eigen_values <- eigen(cov_matrix)$values
eigen_vectors <- eigen(cov_matrix)$vectors
print(eigen_values)
## [1] 1.529991e+05 7.738258e+03 7.494804e+01 4.551851e+00 4.419579e-12
## [6] 3.114202e-13
print(eigen_vectors)
## [,1] [,2] [,3] [,4] [,5]
## [1,] -1.289707e-02 -0.131560133 0.97942701 0.134315337 -0.072164510
## [2,] 8.188149e-04 -0.019605573 -0.12793653 0.643339225 -0.503369496
## [3,] 3.179438e-04 -0.009024801 -0.01847413 -0.334972014 -0.857800240
## [4,] 3.494977e-05 -0.027135529 0.08386896 -0.675118146 -0.068810738
## [5,] -8.349361e-02 0.987380472 0.12816503 0.008883966 -0.029121428
## [6,] -9.964245e-01 -0.081052896 -0.02352452 -0.002084805 0.002684459
## [,6]
## [1,] 0.000000000
## [2,] -0.562121863
## [3,] 0.389293791
## [4,] -0.729184634
## [5,] -0.027483850
## [6,] 0.001939676
cor_matrix <- cor(credit_data)
print(cor_matrix)
## Age Debt YearsEmployed CreditScore ZipCode
## Age 1.0000000 0.2470022 0.4232489 0.75990576 -0.5887359
## Debt 0.2470022 1.0000000 0.2237872 0.18698295 -0.6994434
## YearsEmployed 0.4232489 0.2237872 1.0000000 0.89711754 -0.7255806
## CreditScore 0.7599058 0.1869829 0.8971175 1.00000000 -0.7742466
## ZipCode -0.5887359 -0.6994434 -0.7255806 -0.77424657 1.0000000
## Income 0.3452272 -0.1158264 -0.1009278 0.01028446 0.3347370
## Income
## Age 0.34522724
## Debt -0.11582643
## YearsEmployed -0.10092775
## CreditScore 0.01028446
## ZipCode 0.33473701
## Income 1.00000000